R yıllar içinde çok fazla değişim gösterdi
https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x
:scale 30%
http://www.youtube.com/watch?v=XcBLEVknqvY
What is R?
https://www.rstudio.com/products/rstudio/download/
https://moderndive.com/2-getting-started.html
https://cran.r-project.org/web/packages/addinslist/README.html
https://rstudio.github.io/rstudioaddins/
devtools::install_github("rstudio/addinexamples", type = "source")
Aynı şeyi çok fazla şekilde yapmak mümkün
R Syntax Comparison::CHEAT SHEET
https://www.amelia.mn/Syntax-cheatsheet.pdf
#RStats — There are always several ways to do the same thing… nice example on with the identity matrix by @TeaStats https://t.co/O3GXdPiM32
— Colin Fay 🤘 (@_ColinFay) April 1, 2019
I love the #rstats community.
— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.
Available CRAN Packages By Name
https://cran.r-project.org/web/packages/available_packages_by_name.html
CRAN Task Views
https://cran.r-project.org/web/views/
Bioconductor
https://www.bioconductor.org
RecommendR
http://recommendr.info/
pkgsearch
CRAN package search
https://github.com/metacran/pkgsearch
CRANsearcher
https://github.com/RhoInc/CRANsearcher
Awesome R
https://awesome-r.com/
install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")
# install.packages("tidyverse", dependencies = TRUE)
# install.packages("jmv", dependencies = TRUE)
# install.packages("questionr", dependencies = TRUE)
# install.packages("Rcmdr", dependencies = TRUE)
# install.packages("summarytools"):scale 80%
RDocumentation https://www.rdocumentation.org
R Package Documentation https://rdrr.io/
GitHub
Stackoverflow
How I use #rstats
— Emily Bovee (@ebovee09) August 10, 2018
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr
[R] yazmak da işe yarayabiliyor.http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf
https://www.rstudio.com/resources/cheatsheets/
https://github.com/qinwf/awesome-R#readme
https://twitter.com/hashtag/rstats?src=hash
Got a question to ask on @SlackHQ or post on @github? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by @JennyBryan) #rstat pic.twitter.com/gpuGXpFIsX
— ZhiYang (@zhiiiyang) October 18, 2018
https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects
https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio
Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?
— Leonard Kiefer (@lenkiefer) July 7, 2018
View(data)
data
head
tail
glimpse
str
skimr::skim()
:scale 30%
questionr paketi kullanılacak
:scale 30%
https://juba.github.io/questionr/articles/recoding_addins.html
summary()
mean
median
min
max
sd
table()
library(readr)
irisdata <- read_csv("data/iris.csv")
jmv::descriptives(
data = irisdata,
vars = "Sepal.Length",
splitBy = "Species",
freq = TRUE,
hist = TRUE,
dens = TRUE,
bar = TRUE,
box = TRUE,
violin = TRUE,
dot = TRUE,
mode = TRUE,
sum = TRUE,
sd = TRUE,
variance = TRUE,
range = TRUE,
se = TRUE,
skew = TRUE,
kurt = TRUE,
quart = TRUE,
pcEqGr = TRUE)# install.packages("scatr")
scatr::scat(
data = irisdata,
x = "Sepal.Length",
y = "Sepal.Width",
group = "Species",
marg = "dens",
line = "linear",
se = TRUE)https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html
with(tobacco,
print(ctable(smoker, diseased, prop = 'n', totals = FALSE),
omit.headings = TRUE, method = "render"))descr(iris,
stats = c("mean", "sd", "min", "med", "max"),
transpose = TRUE,
headings = FALSE,
style = "rmarkdown")# First save the results
iris_stats_by_species <- by(data = iris,
INDICES = iris$Species,
FUN = descr, stats = c("mean", "sd", "min", "med", "max"),
transpose = TRUE)
# Then use view(), like so:
view(iris_stats_by_species, method = "pander", style = "rmarkdown")data(tobacco) # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco,
by(BMI, age.gr, descr,
stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")BMI_by_age <- with(tobacco,
by(BMI, age.gr, descr, transpose = TRUE,
stats = c("mean", "sd", "min", "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)